You Need ModelOps To Scale

May 18, 2020

Jun Wu, Towards Data Science – April 29, 2020

As companies, particularly large organizations, scale up their models as a part of building an enterprise-wide pipeline, there’s an increasing need to operationalize the model development process. Similar to DevOps, models need to be developed, integrated, deployed and monitored. Often, with Enterprise AI initiatives, there are a host of governance considerations such as data integrity, change management, regulatory concerns, etc..

You want to be able to connect your data science pipeline to the IT organization. You want your IT organization to maintain and upgrade your data science pipeline as needed.

In other words, you want your pipeline to be fully functional across your organization, in real-time, and in line with your industry’s regulation.

All ModelOp Blog Posts

Jun Wu, Forbes – March 31, 2020 In the last two years, large enterprise organizations have been scaling up their artificial intelligence and machine learning efforts. To apply models to hundreds of use-cases, organizations need to operationalize their machine learning...

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Mark Labbe, TechTarget – April 16, 2020 Exos, a provider of institutional finance services and vendor of a platform for B2B institutional finance, doesn't have a big staff, but it's getting bigger. The privately held firm, founded in 2018, has about 65 employees, and...

ModelOp Raises $6 Million in Series A Funding from Valley Capital Partners to Meet Increasing Demand for Foundational ModelOps Capabilities for Enterprise AI New Funding Solidifies and Extends Leadership in Operationalizing AI and Machine Learning Models at...